The dynamic carbon footprint modeling for laser direct metal deposition based on processing states

Abstract

The Laser Direct Metal Deposition (LDMD) process offers notable advantages and obtains wide application in aerospace, defence equipment and other industrial sectors. However, the characteristics of multiple-inputs and low-efficiency in the process will bring about high carbon footprint. Additionally, the carbon footprint fluctuates with processing states and equipment systems owing to the discontinuous process in LDMD involving the layer-by-layer deposition and the laser-powder coupling. This paper investigated dynamic carbon footprint based on the resource and energy consumption from the process-oriented standpoint in LDMD process, which quantified the processing time, power distribution and material consumption of each processing state for every sub-system. In this research, the discontinuous process was divided into effective processing state, rapid moving state and transformed delay state and the processing time of each state were estimated with process parameters. Besides, the operating state time and power distribution of five sub-systems were estimated combining the equipment triggering mechanism and the processing state time. The orthogonal experiment was carried out to deposit the 316L stainless steel powder into parts with the size of 15 mm*15 mm*10 mm under the given process parameter conditions (Vf ∈ [8,12] g/min, P ∈ [600,1000] w, Vs ∈ [500,900] mm/min). The results show that carbon footprint of deposited parts fluctuates in the range of 3624.68–15759.08 g under parameter combinations and the low carbon footprint parameter-interval is obtained. The average carbon footprint of effective processing state, the transformed delay state and the rapid moving state account for 55%, 40% and 5% respectively, while the carbon footprint of powder supply sub-system contributes to 68% among the five sub-systems. This research revealed and qualified the dynamicity of carbon footprint at the levels of processing states, process parameters and sub-systems, offering potential for carbon footprint reduction in advanced manufacturing processes.